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Machine Learning-Based Forecasting of Daily Acute Ischemic Stroke Admissions Using Weather Data

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Link to Profile David Rügamer PI Matchmaking

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Abstract

Background: In the midst of the emerging climate crisis, healthcare providers lack locally validated, disease-specific surveillance models. Stroke, a significant contributor to the global disease burden, has been linked to climate change. Therefore, we developed and benchmarked machine learning (ML) models based on locoregional weather systems to forecast the number of daily acute ischemic stroke (AIS) admissions.<br>Methods: AIS patients diagnosed between 2015 and 2021 at the tertiary University Medical Center (UMC) Mannheim, Germany were extracted from the local data integration center and geospatially matched to weather data from the German Weather Service (DWD) based on the clinic’s, patients’ home and closest tower’s locations at the time of admission. Statistical-(Poisson), boosted generalized additive model (GAM), support vector machines (SVR), and tree-based models including random forest (RF) and extreme gradient boosting (XGB) were evaluated in regression settings within time-stratified nested cross-validation setup (training-validation: 2015-2020, test set: 2021) to predict the number of daily AIS admissions.<br>Findings: The cohort included 7,914 AIS patients (4,244 male, 53·6%). XGB showed the best test performance with lowest mean absolute error (MAE) of 1·21 cases/day. Maximum air pressure was identified as the top predictive variable. Shapley additive explanations analyses revealed that temperature extremes of extended cold- (lag-3 minimum temperature <-2 °C; minimum perceived temperature <-1·4 °C) and hot stressors (lag-7 minimum temperature >15 °C), as well as stormy conditions (lag-1 and lag-2 maximum wind gust >14 m/s and speed >10·4 m/s), increased stroke incidences substantially with distinct seasonal associations.<br>Interpretation: ML models can sufficiently forecast AIS admissions based on weather patterns allowing for improved resource allocation and preparedness.

article

npj Digital Medicine

8.225. Apr. 2025.
Top Journal

Authors

N. Santhanam • H. E. Kim • D. Rügamer • A. Bender • S. Muthers • C. G. Cho • A. Alonso • K. Szabo • F.-S. Centner • H. Wenz • T. Ganslandt • M. Platten • C. Groden • M. Neumaier • F. Siegel • M. E. Maros

Links

DOI

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: SKR+25

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